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ISSN Online: 1945-3108 ISSN Print: 1945-3094

DOI: 10.4236/jwarp.2018.102010 Feb. 28, 2018 167 Journal of Water Resource and Protection

Evaluating Discharge Capacity of Major Chara’s

of Sylhet City Using GIS

Gulam Md. Munna

*

, Jahir Bin Alam, Jobayer Ahmed, Abdullah Al Mamun

Department of Civil and Environmental Engineering, Shahjalal University of Science and Technology (SUST), Kumargaon, Sylhet-3114, Bangladesh

Abstract

Heavy rainfall is one of the most frequent and widespread severe weather ha-zards that affect Sylhet city. In Sylhet, nine natural channels locally called “chara” are mainly responsible for the drainage of heavy rainfall to the Surma River. The present condition of this natural drainage is not feasible due to the unplanned land development and manipulation by people. So water logging is a common scenario of Sylhet city. Many parts of Sylhet city are experiencing the severe inundation problem due to heavy rainfall. Time series analysis of rainfall data (1974-2015) is important for knowing and predicting of rainfall variation. Mann-Kendall analysis showed no trend for monthly and yearly rainfall data. The rainfall intensity of Sylhet is higher than other districts of Bangladesh. In this study, IDF (Intensity Duration Frequency) curve has been developed that is commonly used in engineering planning and design. By us-ing IDF curve, ArcGIS software, DEM map, and normal discharge, maximum discharge for a different point of Mongoli chara and Bolramer chara has been calculated (for 25 years return period). Discharge through Mongoli chara and Bolramer chara has been represented in ArcMap using ordinary kriging. To keep the catchment of those chara free from inundation and water logging problem, this calculated discharge needed to be managed.

Keywords

ArcGIS, Canal, Chi-Square, Gamble Distribution, IDF Curve, Mann-Kendall, Rainfall

1. Introduction

Bangladesh is affected by various types of natural disasters almost every year be-cause of the global warming as well as climate change impacts. Bangladesh is a low-lying country with more than 230 waterways, is one of the most

disas-How to cite this paper: Munna, G.M., Alam, J.B., Ahmed, J. and Al Mamun, A. (2018) Evaluating Discharge Capacity of Major Chara’s of Sylhet City Using GIS. Journal of Water Resource and Protection, 10, 167-181.

https://doi.org/10.4236/jwarp.2018.102010

Received: December 7, 2017 Accepted: February 25, 2018 Published: February 28, 2018

Copyright © 2018 by authors and Scientific Research Publishing Inc. This work is licensed under the Creative Commons Attribution International License (CC BY 4.0).

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DOI: 10.4236/jwarp.2018.102010 168 Journal of Water Resource and Protection

ter-prone nations in the world [1]. Fifteen percent of its land floods annually on average [2]. As the lowest riparian in a huge trans-boundary river basin, Ban-gladesh faces increasing threat of massive flood exposure [3].

Floods are a natural and certain event. Natural flood management includes techniques and process that aim to work with natural hydrological and mor-phological processes, features and characteristics to deal with the sources and pathways of flood waters.

The outcome of urbanization is broadly known as hydraulic studies since there have been increasing problems in urban storm water drainage manage-ment. One of the most important facilities in preserving and improving the ur-ban environment is an adequate and properly functioning storm water drainage system which includes storm water movement and storage capacity.

Numerous catchments in Bangladesh are currently under serious pressure from urban, industrial and infrastructure development where downstream receiving water bodies such as river, lakes, channels, pond and reservoirs have turned out to be touchy to build rates and volumes of overflow and contamination release.

Inadequate drainage causes needless death and disease and loss of home, property and live hoods. Poor storm water management also pollutes the envi-ronment and squanders limited freshwater resource. A sound investment in storm water can reduce those losses, but only after setting priorities and making different choices [4][5].

Integrated storm water is still a moderately new idea in many countries in-cluding Bangladesh. Present experience indicates that end-of-pipe, rapid dispos-al, localized reactive and mono-functional drainage concepts have been generally practiced in Bangladesh. The local design makes and professionals are beginning to recognize the need for a new and broader approach to urban drainage, flood control and storm water management in the light of development in the country processing at a tremendous stress.

2. Study Area

Our study areas are Mongoli Chara and Bolramer Chara in Sylhet City (Figure 1) of Bangladesh. The local design makes and professionals are beginning to recognize the need for a new and broader approach to urban drainage, flood control and storm water management in the light of development in the country processing at a tremendous stress.

3. Materials and Methods

The study area contains many rainfall stations of Bangladesh Meteorological Department (BMD). The daily rainfall data was collected from BMD for the years 1973 up to 2015 (Total 41 years). For the continuity of the data, the entire record of gauging station was verified.

3.1. Trend Analysis

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[image:3.595.209.538.68.458.2]

DOI: 10.4236/jwarp.2018.102010 169 Journal of Water Resource and Protection Figure 1. Mongoli Chara and Bolramer Chara in Sylhet, Bangladesh. Mongoli Chara: It flows from Mirer Moidan road to Old Medical, cross the Chouhatta-Rikabi bazaar road and flows southward to Soros pur, Jallar par, Kazir bazaar, Sheik Ghat and Taltota. Fi-nally the chara falls into Surma River crossing the Kazir bazaar-Tookhana road. Bolra-mer Chara: BolraBolra-mer Chara is flowing from Jamtala (south of Jallar par road), Bondar bazaar and Taltota. One part of it meets with Mongoli Chara near the Mohan Market and another part falls on Surma River near Kazir bazaar.

series. The purpose of the Mann-Kendall (MK) test [6] is to statistically assess if there is a monotonic increase or decrease trend of the rainfall of 1974 to 2015

[7].

3.2. Development of Short Duration Rainfall Data

Short duration data are rare in development countries like Bangladesh. The data of 24-hour rainfall records are available in rain gauge stations.

Indian Meteorological Department (IMD) uses an empirical reduction for-mula [8].

[

]

1 3

24 24

t

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DOI: 10.4236/jwarp.2018.102010 170 Journal of Water Resource and Protection

where, Pt is the required rainfall depth in mm at t hr. duration, P24 is the daily

rainfall in mm and t is the duration of rainfall for which the rainfall depth is re-quired in hr.

For estimation of various duration like 1-hr rainfall values from annual max-imum daily rainfall data, Chowdhury et al.[8], used Indian Meteorological De-partment (IMD) empirical reduction formula to estimate the short duration rainfall from daily rainfall data in Sylhet city and found that this formula gives the best estimation of short duration rainfall [9]. In this study, this empirical formula was used to estimate the short duration rainfall in SCC.

3.3. Probability Distribution and Chi-Square Test

To identify a specific theoretical distribution for the available data it is important to find an acceptable method. The aim of the test is to find how good a fit is the observed and the predicted data. Chi-square is one of the most widely used tests to find the best fit theoretical distribution of any specific dataset which is represented by this formula [10].

(

)

2

2 1

n i i i i O E X E = −

=

(2)

where, Oi and Ei represent the observed and expected frequencies

respec-tively. If the observed frequencies are close to the corresponding expected fre-quencies, the 2

X value will be small, indicating a good fit; otherwise it will be a

poor fit. Chi-Square test was performed by sigma magic software.

3.4. Short Duration for Gumbel Distribution

Short duration rainfall data such as 5 min, 10 min, 15 min, 30 min, 45 min, 120 min have been developed using the depth-duration formula [10].

(

)

0.25

60

0.54 0.50 5 120 min

T t

T P

t t

P = − ≤ ≤ (3)

(

)

10 0.21ln 0.52 2 100 years

T t

t P

T T

P = − ≤ ≤ (4)

where, T t

P is the depth of t-minute, T-year return period rainfall, 10

t

P is the

depth of a t-minute, 10-year return period rainfall and 60

T

P is the depth of a

60-minute, T-year return period rainfall.

3.5. Development of Rainfall Intensity Duration Frequency (IDF)

Curve

IDF curve is used for estimating the maximum rainfall intensity for the different duration and return period. A specified return period T and duration t is calcu-lated for the rainfall depth. Its mean intensity Im (mm/hour) has been

ob-tained dividing it by the duration t (hour). Then the IDF curve is obtain by plot-ting, on a graph, the main intensity Im (mm/hour) against the duration D

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DOI: 10.4236/jwarp.2018.102010 171 Journal of Water Resource and Protection

Mathematically, this curve can be represented in different forms as follows

[9]:

(

)

n

a i

b D

=

+ (5)

( )

y

i= ∗x D − (6)

where, i is rainfall intensity in mm/hr, D is duration of rainfall in minutes, the parameters a, b, n, x, y define the shape and appropriate units for curve fitting to IDF data.

3.6. Catchment Area of Chara

The catchment area of Mongoli Chara and Bolramer Chara has been identified through field survey and contour map. The direction of runoff flow has been identified during field survey and consulting local people. And then the catch-ment area was divided into some regions.

3.7. Runoff Coefficient

The runoff coefficient C is the variable of the rational method. The runoff coeffi-cient (C) is a dimensionless coefficient relating the amount of runoff to the amount of precipitation received. It is a larger value for areas with low infiltra-tion and high runoff, and lower for permeable, well-vegetated areas [11]. Where watershed is not homogeneous, a weighted runoff coefficient should be deter-mined. A weighted runoff coefficient is computed using the following equation

[9]: 1 1 n j j w j n j j C A C A = = =

(7)

where, Aj is the area for land cover j, Cj is the runoff coefficient for area j, N

is the number of distinct land covers within the watershed, and Cw is the

weighted runoff coefficient.

3.8. Time of Concentration

Time of concentration (Tc) is the time required for runoff to travel from the

hy-draulically most distant point in the watershed to the outlet. The hyhy-draulically most distant point is the point with the longest travel time to the watershed out-let, and not necessarily the point with the longest flow distance to the outlet. Time of concentration is generally applied only to surface runoff and may be computed using many different methods. Time of concentration will vary de-pending upon slope and character of the watershed and the flow path characte-ristics [12][13].

The U.S. Federal Aviation Agency (FAA) developed a simple estimation of Tc

to be used with the Rational Method [14]. Time of concentration (Tc) is an

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Natu-DOI: 10.4236/jwarp.2018.102010 172 Journal of Water Resource and Protection

ral Resources Conservation Service procedures. For the same size watershed, the shorter the Tc, the larger the peak discharge [14].

(

)

3

1.8 1.1 c

C L

T

S

∗ − ∗

= (8)

where, C is the cover factor, L is the hydraulic length in ft, and S is the slope in Percent.

3.9. Discharge Due to Rainfall

The rational formula has developed the relation between rainfall and discharge. Discharge due to rainfall calculated by this formula [15].

360 r

C i A

Q = ∗ ∗ (9)

where, Qr: Peak discharge (m

3/s) C: Runoff Coefficient. i: Rainfall intensity during time of concentration (mm/hr).

A: Drainage Area (ha).

3.10 Discharge Due to Population

There are many formulas for population prediction. The geometric mean in-crease is used to find out the future increment in population. Assuming that, the future population is increasing at a constant rate. Geometric increase formula

[16] is:

1 100

n

n o r

p =p + 

  (10)

Here, pn = population after n year, po = Present population, r = rate of

growth and n = number of year. Discharge Due to Population [14]:

P

Q = ∗X P (11)

where, QP = Discharge due to population, X = Per capita discharge and P =

Total population.

4. Result and Discussion

4.1. Monthly Rainfall Trend Analysis

There are several statistical tests available for testing stationeries of time series. In the present study, the Man-Kendell test (Table 1) for linear trend has been carried out for the monthly rainfall series from 1974 to 2015 of Sylhet.

Table 1. Trend analysis.

Kendall’s tau S’ P-value (one-tailed) Alpha

−0.0619 −535 0.9766 0.05

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DOI: 10.4236/jwarp.2018.102010 173 Journal of Water Resource and Protection

that the observed frequencies are near to the corresponding expected frequencies.

4.2. Chi-Square Test

[image:7.595.208.540.164.438.2]

Chi-Square test has been performed by 1-hr rainfall data that shown in Table 2.

Table 2. Chi-square test.

Distribution Chi-Square values P Fit

Beta 6.52 0.368 Ok

Cauchy 5.87 0.438 Ok

Erlang 6.58 0.362 Ok

Gumbel 3.43 0.753 Ok

Exponential 10.06 0.122 Ok

Gamma 5.46 0.486 Ok

Laplace 12.54 0.051 Ok

Log 4.94 0.551 Ok

Logistic 8.66 0.194 Ok

Log 6.15 0.406 Ok

Normal 11.94 0.063 Ok

Pareto 9.99 0.125 Ok

Power 11.7 0.069 Ok

Rayleigh 8.25 0.221 Ok

Weibull 732.09 <0.001

Beta distribution, Cauchy distribution, Gamma distribution, Rayleigh distri-bution, Logistic distridistri-bution, Gumbel distribution fits the data. Chi-Sq. value is

low for Gumbel distribution, so the Gumbel distribution best fits the selected data.

4.3 Gumbel Distribution

1-hour 2, 5, 10, 25, 50, 100-year rainfall depth for Sylhet is estimated using Gumbel distribution formula which has been show in Table 3.

Table 3. 1-hour rainfall depths at different rainfall periods.

T (year) K Pavg (mm) S (mm) PT (mm)

2 0.16427

69.68 17.26

66.84

5 0.71945 82.10

10 1.30456 92.20

25 2.04385 104.96

50 2.59229 114.43

[image:7.595.208.539.605.732.2]
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DOI: 10.4236/jwarp.2018.102010 174 Journal of Water Resource and Protection

Rainfall depth depends on return period. While return period is 2 year rainfall depth is low. But with the increase of return period rainfall depth increase si-multaneously. In 2 year return period rainfall depth is 66.847 mm and in 100 year return period rainfall depth is 123.82 mm.

4.4. Development of Rainfall Intensity Duration

Within same return period rainfall depth depends on rainfall duration. Rainfall depth is increased with the increase of rainfall duration. In 2 year return period for 5 min and 120 min rainfall depth is 20.55474 mm and 86.05012 mm respec-tively. Average intensities of rainfall for different return periods have been cal-culated by dividing t minute rainfall depths by the corresponding duration of t

[image:8.595.207.540.284.404.2]

(hour) (Table 4).

Table 4. Rainfall depths of Sylhet for t-minute.

Return period

Rainfall depth (mm)

5 min 10 min 15 min 30 min 60 min 120 min 2 year 20.55474 30.76783 37.61590 51.05708 66.84722 86.05012 5 year 25.24534 37.78905 46.19985 62.70831 82.10177 105.6868 10 year 28.35097 42.43780 51.88327 70.42257 92.20178 118.6882 25 year 32.27496 48.31151 59.06431 80.16960 104.9632 135.1155 50 year 35.18596 52.66891 64.39155 87.40039 114.4302 147.3021 100 year 38.07547 56.99413 69.67944 94.57779 123.8274 159.3987

4.5. Intensity-Duration-Frequency (IDF) Curve

Short duration rainfall data such as 5 min, 10 min, 15 min, 30 min, 45 min, 120 min for 2, 5, 10, 25, 50, 100-year have been developed using the depth-duration formula. Within same return period, rainfall intensity depends on rainfall dura-tion. Rainfall Intensity decrease with the increase of rainfall duradura-tion. In 2 year return period for 5 min and 120 min rainfall Intensity are 246.6569 mm and 43.02506 mm respectively. For the same duration, rainfall intensity depends on rainfall duration. Rainfall intensity is increasing with the increase of return pe-riod. For 30 min duration 2 years and 100 years rainfall intensity is 102.1142 mm and 189.1556 mm. Intensity-Duration-Frequency (IDF) curve has been devel-oped by plotting average intensities against the duration of rainfall. IDF curve for Sylhet has been shown in Figure 2.

For same year return period, rainfall intensity decrease with the increase of time duration. For same time duration rainfall intensity is higher for higher re-turn period. It was observed in the study that the IDF empirical equation form

( )

y

i= ∗x D − had the best fit rather than the equation form

(

)

n

a

b D

=

+ .

4.6. Derivation of IDF Equation

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[image:9.595.61.534.70.313.2]

DOI: 10.4236/jwarp.2018.102010 175 Journal of Water Resource and Protection Figure 2. IDF curve for Sylhet city.

or the station, there are some required steps for establishing an equation suit the calculation of rainfall intensity for a certain recurrence interval and specific rainfall period which depends mainly on the results obtained from the intensity duration frequency (IDF) curves and the corresponding logarithmic conversion, where it is possible to convert the equation into a power equation (Table 5), and thus to calculate all the parameters related to the equation.

Table 5. Rainfall IDF curve equation and regression value for Sylhet city.

2 year 5 year 10 year 25 year 50 year 100 year

i =

647.15D−0.556 794.84Di = −0.556 892.61Di = −0.556 1016.2Di = −0.556 1107.8Di = −0.556 1198.8Di = −0.556

R2 = 0.9944 R2 = 0.9944 R2 = 0.9944 R2 = 0.9944 R2 = 0.9944 R2 = 0.9944

100 year rainfall intensity is higher than 2 year rainfall intensity. Rainfall in-tensity equation format is

( )

y

i= ∗x D − . R2 value is same for every rerun period.

4.7. Catchment Area Selection & Dividing into Sub-Areas

Catchment Area of was selected by using “Polygon” tools and then converted it to “Shape file” by using “Feature to Polygon” tools of ArcGIS. Separation into sub-area was done by the same process (Figure 3).

4.8. Development of Digital Elevation Model (DEM)

[image:9.595.208.538.471.541.2]
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[image:10.595.208.541.71.470.2]

DOI: 10.4236/jwarp.2018.102010 176 Journal of Water Resource and Protection Figure 3. Catchment area of Chara.

4.9. Time of Concentration

Time of concentration (Tc) of six zone was estimated by Federal Aviation

Agency (FAA) formula (Table 6). Length of flow and average slope was found from ArcMap. Length of flow and average slope is calculated from Shape file and DEM map. Time of concentration is highest at zone 3 and lowest at zone 6.

Table 6. Estimation of time of concentration using FAA formula.

Zone coefficient Rational Length of flow (km) slope (%) Average Length of flow (ft) Time of concentration (min)

1 0.574 0.91 1.6 2985.5644 44.23841076

2 0.574 1.34 1.5 4396.3256 54.84850377

3 0.574 1.31 1.3 4297.9004 56.87786504

4 0.574 1.10 1.8 3608.9240 46.76720045

5 0.574 1.06 2.5 3477.6904 41.15188039

[image:10.595.198.539.610.736.2]
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[image:11.595.210.538.67.484.2]

DOI: 10.4236/jwarp.2018.102010 177 Journal of Water Resource and Protection Figure 4. DEM of Sylhet city.

4.10. Discharge Due to Rainfall

Discharge was calculated by the rational formula. Rainfall intensity has been calculated from IDF curve for the duration equal to the time of concentration. 25 year return period has been selected for calculating rainfall calculated discharge has been given in below. Drainage area is calculated from Shape file. At zone 1 discharge is maximum as it has largest area, at zone 5 discharge is minimum as its catchment area is lowest. Peak discharge is the sum of discharge of this zone and peak discharge of previous zone. Peak discharge at zone 4 is the sum of dis-charge of zone 4, zone 6 and peak disdis-charge of zone 3.

4.11. Discharge Due to Population

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DOI: 10.4236/jwarp.2018.102010 178 Journal of Water Resource and Protection

[image:12.595.205.539.133.246.2]

discharge of this zone and peak discharge of previous zone. Peak discharge at zone 4 is the sum of discharge of zone 4, zone 6 and peak discharge of zone 3 (Table 8).

Table 7. Discharge from different zone.

Zone Discharge from zone (m3/s) Peak discharge (m3/s)

1 0.029874421 0.029874421

2 0.015490394 0.045364815

3 0.028400116 0.073764931

4 0.018810764 0.106222569

5 0.010695718 0.116918287

[image:12.595.208.538.279.424.2]

6 0.013646875 0.013646875

Table 8. Calculated peak discharge.

Zone coefficient Rational concentration Time of (min)

Rainfall intensity (mm/hr)

Drainage area (Sq km)

Discharge (m3/s)

Peak discharge

(m3/s)

1 0.574 44.23841076 123.570458 0.81 15.9591247 15.959124 2 0.574 54.84850377 109.648721 0.42 7.34280935 23.301934 3 0.574 56.87786504 107.456013 0.77 13.1926135 36.494547 4 0.574 46.76720045 119.809647 0.51 9.74252109 54.807927 5 0.574 41.15188039 128.640739 0.29 5.94820483 60.756132 6 0.574 33.06459851 145.282472 0.37 8.57085874 8.5708587

4.12. Total Discharge

Total discharge is the sum of discharge due to rainfall and discharge due to the population. Table 9 shows the peak discharge for different zones, which is maximum (60.87 m3/s) at zone 5 and minimum (8.58 m3/s) at zone 6.

Table 9. Design discharge.

Zone Peak discharge (m3/s)

1 15.98899918

2 23.34729892

3 36.56831257

4 54.91415004

5 60.87305059

6 8.584505613

4.13. Discharge in Chara

From the equation the corresponding discharge at different point of Chara in m3/s was obtained and plot them in the ArcMap software by using “kriging”

[image:12.595.207.539.535.633.2]
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[image:13.595.209.541.67.487.2]

DOI: 10.4236/jwarp.2018.102010 179 Journal of Water Resource and Protection Figure 5. Discharge in Chara.

4.14. Existing and Required X-Sectional Area

The required cross-sectional area has been calculated which is shown in Table 10. Before meeting with Bolramer chara the existing x-section of Mongoli chara satisfy the required cross section. But after meeting with Bolramer chara the ex-isting cross section is not sufficient.

Table 10. Existing & required x-sectional area.

Location Existing X-sectional area (m2) Required X-sectional area (m2)

Kazir Bazar (before meeting with

Bolramer chara) 25.50 21.0

Kazir Bazar (after meeting with

[image:13.595.209.538.633.736.2]
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DOI: 10.4236/jwarp.2018.102010 180 Journal of Water Resource and Protection

5. Conclusions

Based on the result of the study, following conclusions were made:

There is no trend in rainfall that could be detected at the 5% significance level

in Sylhet.

Average intensities of rainfall for 25 years return periods have been calculated.

Average intensities for 5 min, 10 min, 15 min, 30 min, 60 min, 120 min are 387.2996, 289.8691, 236.2573, 160.3392, 104.9632, 67.55776 (mm/hr) respec-tively.

Existing cross section area of chara at Kazir Bazar (before meeting with

Bo-lramer Chara) & Kazir Bazar (after meeting with BoBo-lramer Chara) has been found 25.5 m2, 34.96 m2 and required X-sectional area 21 m2, 37.5 m2

respec-tively. Peak discharges of Mongoli Chara and Bolramer Chara have been cal-culated to 60.87 m3/s and 8.58 m3/s respectively for 25 years return period.

The required cross-sectional area has been calculated. Before meeting with

Bolramer Chara, the existing x-section of Mongoli chara satisfies the required cross section. But after meeting with Bolramer Chara, the existing cross sec-tion is not sufficient.

References

[1] Development, GBPHOCI. DFID’s Programme in Bangladesh. 2010.

[2] Memon, N. (2012) Disasters in South Asia: A Regional Perspective. Pakistan Insti-tute of Labour Education & Research.

[3] Mozumder, P. (2010) Exploring Flood Mitigation Strategies in Bangladesh. [4] Parkinson, J. and Mark, O. (2005) Urban Stormwater Management in Developing

Countries. 1st Edition, IWA Publishing, London, UK.

[5] Kar, K.K., Yang, S.K., Lee, J.H. and Khadim, F.K. (2017) Regional Frequency Analy-sis for Consecutive Hour Rainfall Using L-Moments Approach in Jeju Island, Ko-rea. Geoenvironmental Disasters, 4, 1-13.

https://doi.org/10.1186/s40677-017-0082-0

[6] Mann, H.B. (1945) Nonparametric Test against Trend. Econometrica, 13, 245-259.

https://doi.org/10.2307/1907187

[7] Mishra, N. and Gupta, S. (2015) Precipitation Trend Analysis by MK Test in Garh-wal Region Districts of Uttarakhand (1901-2010). Journal of Space Science & Tech-nology, 4, 25-35.

[8] Chowdhury, R.K., Alam, M.J.B., Das, P. and Alam, M.A. (2007) Short Duration Rainfall Estimation of Sylhet: IMD and USWB Method. Journal-Indian Waterworks Association, 39, 285.

[9] Rashid, M.M., Faruque, S.B. and Alam, J.B. (2012) Modelling of Short Duration Rainfall Intensity Duration Frequency (SDRIDF) Equation for Sylhet City in Ban-gladesh. ARPN Journal of Science and Technology, 2, 92-95.

[10] Nhat, L.M., Tachikawa, Y. and Takara, K. (2006) Establishment of Intensity-Duration- Frequency Curves for Precipitation in the Monsoon Area of Vietnam. Annuals of Disas. Prev. Res. Inst., Kyoto Univ., No. 49 B, 2006.

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[12] Solomon, K.R., Baker, D.B., Richards, R.P., et al. (1996) Ecological Risk Assessment of Atrazine in North American Surface Waters. Environmental Toxicology and Chemistry, 15, 31-76. https://doi.org/10.1002/etc.5620150105

[13] Kar, K.K., Yang, S.K. and Lee, J.H. (2015) Assessing Unit Hydrograph Parameters and Peak Runoff Responses from Storm Rainfall Events: A Case Study in Hancheon Basin of Jeju Island. Journal of Environmental Science International, 24, 437-447.

https://doi.org/10.5322/JESI.2015.24.4.437

[14] Matthew, A.P. (2009) Engineering 101: The FAA Equation-Time of Concentration.

http://www.civil4d.com/2009/03/engineering-101-the-faa-equation-time-of-concent ration/

[15] Wong, T.S. (2002) Call for Awakenings in Storm Drainage Design. Journal of Hy-drologic Engineering, 7, 1-2. https://doi.org/10.1061/(ASCE)1084-0699(2002)7:1(1)

Figure

Figure 1. Mongoli Chara and Bolramer Chara in Sylhet, Bangladesh. Mongoli Chara: It mer Chara: Bolramer Chara is flowing from Jamtala (south of Jallar par road), Bondar bazaar and Taltota
Table 3. 1-hour rainfall depths at different rainfall periods.
Table 4. Rainfall depths of Sylhet for t-minute.
Table 5. Rainfall IDF curve equation and regression value for Sylhet city.
+5

References

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